As foundation models (FMs) play an increasingly prominent role in complex software systems, such as agentic software, they introduce significant observability and debuggability challenges. Although recent Large Reasoning Models (LRMs) generate their thought processes as part of the output, in many scenarios fast-thinking Large Language Models (LLMs) are still preferred due to latency constraints. LLM-powered agents operate autonomously with opaque implicit reasoning, making it difficult to debug their unexpected behaviors or errors. In this paper, we introduce Watson, a novel framework that provides reasoning observability into the implicit reasoning processes of agents driven by fast-thinking LLMs, allowing the identification and localization of errors and guidance for corrections. We demonstrate the accuracy of the recovered implicit reasoning trace by Watson and its usefulness through debugging and improving the performance of LLM-powered agents in two scenarios: Massive Multitask Language Understanding (MMLU) benchmark and SWE-bench-lite. Using Watson, we were able to observe and identify the implicit reasoning errors, and automatically provide targeted corrections at runtime that improve the Pass@1 of agents on MMLU and SWE-bench-lite by 7.58 (13.45% relative improvement) and 7.76 (12.31% relative improvement) percentage points, respectively, without updates to models or the cognitive architecture of the agents.

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